The Utility of Deformable Image Registration for Small Artery Visualisation in Contrast-Enhanced Whole Body MR Angiography

Purpose; An investigation was carried out into the effect of three image registration techniques on the diagnostic image quality of contrast-enhanced magnetic resonance angiography (CE-MRA) images. Methods Whole-body CE-MRA data from the lower legs of 27 patients recruited onto a study of asymptomatic atherosclerosis were processed using three deformable image registration algorithms. The resultant diagnostic image quality was evaluated qualitatively in a clinical evaluation by four expert observers, and quantitatively by measuring contrast-to-noise ratios and volumes of blood vessels, and assessing the techniques’ ability to correct for varying degrees of motion. Results The first registration algorithm (‘AIR’) introduced significant stenosis-mimicking artefacts into the blood vessels’ appearance, observed both qualitatively (clinical evaluation) and quantitatively (vessel volume measurements). The other two algorithms (‘Slicer’ and ‘SEMI’) based on the normalised mutual information (NMI) concept and designed specifically to deal with variations in signal intensity as found in contrast-enhanced image data, did not suffer from this serious issue but were rather found to significantly improve the diagnostic image quality both qualitatively and quantitatively, and demonstrated a significantly improved ability to deal with the common problem of patient motion. Conclusions This work highlights both the significant benefits to be gained through the use of suitable registration algorithms and the deleterious effects of an inappropriate choice of algorithm for contrast-enhanced MRI data. The maximum benefit was found in the lower legs, where the small arterial vessel diameters and propensity for leg movement during image acquisitions posed considerable problems in making accurate diagnoses from the un-registered images

Respiratory motion correction in dynamic MRI using robust data decomposition registration - Application to DCE-MRI.

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC60) at early enhancement

Alignment of contrast enhanced medical images

The re-alignment of series of medical images in which there are multiple contrast variations is difficult. The reason for this is that the popularmeasures of image similarity used to drive the alignment procedure do not separate the influence of intensity variation due to image feature motion and intensity variation due to feature enhancement. In particular, the appearance of new structure poses problems when it has no representation in the original image. The acquisition of many images over time, such as in dynamic contrast enhanced MRI, requires that many images with different contrast be registered to the same coordinate system, compounding the problem. This thesis addresses these issues, beginning by presenting conditions under which conventional registration fails and proposing a solution in the form of a ’progressive principal component registration’. The algorithm uses a statistical analysis of a series of contrast varying images in order to reduce the influence of contrast-enhancement that would otherwise distort the calculation of the image similarity measures used in image registration. The algorithm is shown to be versatile in that it may be applied to series of images in which contrast variation is due to either temporal contrast enhancement changes, as in dynamic contrast-enhanced MRI or intrinsically in the image selection procedure as in diffusion weighted MRI

Fast registration of contrast-enhanced magnetic resonance images of the breast

Master'sMASTER OF ENGINEERIN

DEFORM'06 - Proceedings of the Workshop on Image Registration in Deformable Environments

Preface These are the proceedings of DEFORM'06, the Workshop on Image Registration in Deformable Environments, associated to BMVC'06, the 17th British Machine Vision Conference, held in Edinburgh, UK, in September 2006. The goal of DEFORM'06 was to bring together people from different domains having interests in deformable image registration. In response to our Call for Papers, we received 17 submissions and selected 8 for oral presentation at the workshop. In addition to the regular papers, Andrew Fitzgibbon from Microsoft Research Cambridge gave an invited talk at the workshop. The conference website including online proceedings remains open, see http://comsee.univ-bpclermont.fr/events/DEFORM06. We would like to thank the BMVC'06 co-chairs, Mike Chantler, Manuel Trucco and especially Bob Fisher for is great help in the local arrangements, Andrew Fitzgibbon, and the Programme Committee members who provided insightful reviews of the submitted papers. Special thanks go to Marc Richetin, head of the CNRS Research Federation TIMS, which sponsored the workshop. August 2006 Adrien Bartoli Nassir Navab Vincent Lepeti

Automatic correspondence between 2D and 3D images of the breast

Radiologists often need to localise corresponding findings in different images of the breast, such as Magnetic Resonance Images and X-ray mammograms. However, this is a difficult task, as one is a volume and the other a projection image. In addition, the appearance of breast tissue structure can vary significantly between them. Some breast regions are often obscured in an X-ray, due to its projective nature and the superimposition of normal glandular tissue. Automatically determining correspondences between the two modalities could assist radiologists in the detection, diagnosis and surgical planning of breast cancer. This thesis addresses the problems associated with the automatic alignment of 3D and 2D breast images and presents a generic framework for registration that uses the structures within the breast for alignment, rather than surrogates based on the breast outline or nipple position. The proposed algorithm can adapt to incorporate different types of transformation models, in order to capture the breast deformation between modalities. The framework was validated on clinical MRI and X-ray mammography cases using both simple geometrical models, such as the affine, and also more complex ones that are based on biomechanical simulations. The results showed that the proposed framework with the affine transformation model can provide clinically useful accuracy (13.1mm when tested on 113 registration tasks). The biomechanical transformation models provided further improvement when applied on a smaller dataset. Our technique was also tested on determining corresponding findings in multiple X-ray images (i.e. temporal or CC to MLO) for a given subject using the 3D information provided by the MRI. Quantitative results showed that this approach outperforms 2D transformation models that are typically used for this task. The results indicate that this pipeline has the potential to provide a clinically useful tool for radiologists